Artificial intelligence for Real-time Guidance of Onboard SAR applications
The Horizon Europe ARGOS project (GA 101293423) will develop a novel end-to-end Artificial Intelligence (AI) framework enabling high-level SAR applications directly from raw data onboard satellites. The project will design a unified onboard processing chain capable of handling SAR raw data and delivering near real-time application-specific inferences in different fields. To make these processes viable in the constrained space environment, ARGOS will focus on optimizing deep learning models, applying multi-tasking and Tiny AI approaches to reduce computational load and power consumption. The framework will be validated on representative space-qualified hardware, ensuring that the solutions can operate effectively under realistic resources. In parallel, the AI-driven approach will be benchmarked against state-of-the-art onboard SAR processing methods. The project will also demonstrate its versatility through demonstration scenarios, including maritime situational awareness, critical areas identification, and environmental monitoring. ARGOS will advance Europe’s autonomy in intelligent EO capabilities, enabling faster, more efficient, and resilient responses to environmental and geopolitical challenges. Ultimately, the project will bridge the gap between algorithmic innovation and operational deployment, serving both environmental and security needs while reinforcing Europe’s leadership in space-based intelligence.
Eurac contributes with multiple different activities to the project, taking a key role in organising the data and AI models as well as project communications, and contributes to AI optimization and model complexity reduction and the case study on soil moisture where one reference site is situated in the Po Valley (northern Italy).
This project will further consolidate EURAC’s experience in onboard applications, AI model management and generally SAR data processing and optimization.
Contact person: Alexander Jacob (alexander.jacob@eurac.edu ),
Claudia Notarnicola (claudia.notarnicola@eurac.edu )
Eurac contributes with multiple different activities to the project, taking a key role in organising the data and AI models as well as project communications, and contributes to AI optimization and model complexity reduction and the case study on soil moisture where one reference site is situated in northern Italy along the Po catchment.
WP1 Problem formulation and scenarios definition
Eurac is contributing with requirements for data and model management as well as the definition of the case study on the Po Catchment related to the Soil Moisture use case.
WP2 Datasets generation
Eurac is responsible for the managing of data of the case studies and designing the workflow to share data related project results with interested stakeholders.
WP3 E2E AI models
Eurac contributes to model architecture and optimization in relation to the soil moisture use case.
WP4 AI models optimization
Eurac contributes to the efforts of minimizing model footprints in terms of required compute and memory resources through tiny models.
Eurac is responsible for the management of AI models through ML flow.
WP5 Hardware demonstration
Eurac has a small role in testing tiny models developed in WP4 on the onboard computer.
WP6 Demo scenarios and budget
WP7 Dissemination and exploitation
Eurac is responsible for general communication and dissemination strategy and coordinating outreach activities of the project to reach relevant stakeholder and user groups.
WP8 Project management
- Project duration: -
- Project status:
- Funding: Horizon Europe (EU funding / Project)
- Institute: Institute for Earth Observation
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